Generative Ai With Python: Core Concepts And Coding Examples

Generative AI with Python: Core Concepts with Practical Coding Examples

What you'll learn

Master Generative AI from scratch – Learn GANs, VAEs, Transformers & Diffusion Models, even as a beginner

Hands-on AI projects – Build text, image & music generation projects to showcase real-world skills

Write industry-ready Python code – Use TensorFlow/Keras, Git, and Docker for clean, reproducible AI projects

Boost career & research opportunities – Learn cutting-edge generative AI to stand out in ML, data science & research

Requirements

Basic Python knowledge, high school-level math, a computer with internet, and enthusiasm to learn AI—no prior AI experience needed.

Description

Step into the future of technology with our hands-on AI and Generative Deep Learning course! From understanding the foundations of AI and probability theory to building advanced neural networks and generative models like GANs, VAEs, and Diffusion Models, this course equips you with the skills to create cutting-edge AI applications.Learn by doing: set up your environment with Git, Docker, and IDEs, implement ANNs, CNNs, LSTMs, and master representation learning. Dive into generative architectures and see your ideas come alive through music generation, advanced GAN projects, and transformer-based applications.Whether you’re an aspiring AI engineer, researcher, or tech enthusiast, this course turns complex concepts into hands-on projects, making you industry-ready. Unlock your potential, create AI-driven solutions, and be part of the next generation of AI innovators!Gain deep insights into probability theory, coding environments, and the latest AI techniques. Explore real-world applications, improve your programming skills, understand model deployment, and learn best practices for optimizing model performance. By the end, you will confidently design, train, and evaluate generative models, turning your ideas into tangible, innovative projects that can impress both academia and industry.Why Enroll?Hands-on projects from setup to deploymentLearn cutting-edge generative AI modelsStep-by-step guidance for real-world applicationsPerfect for beginners and advanced learners alikeEnhance your portfolio with unique, creative AI projects

Overview

Section 1: Foundations of AI & Environment Setup

Lecture 1 Introduction

Lecture 2 GenAI-AI Introduction

Lecture 3 Generative modeling

Lecture 4 Our First Generative Model

Lecture 5 Representative learning

Lecture 6 Core Probability Theory

Lecture 7 Basics of the Coding Environment

Lecture 8 Git clone & Dockers

Lecture 9 Setting up the IDE

Section 2: Deep Learning Fundamentals

Lecture 10 Artificial Neural Networks

Lecture 11 Multilayer Perceptron (MLP)

Lecture 12 Convolutional Neural Networks

Section 3: Generative Modeling & Architectures

Lecture 13 Autoencoders

Lecture 14 Variational Autoencoders

Lecture 15 Generative Adversarial Networks 1 of 2

Lecture 16 Generative Adversarial Networks 2 of 2

Lecture 17 Conditional GAN

Lecture 18 Autoregressive Models LSTM

Lecture 19 RNN Extentions PixelCNN

Lecture 20 Normalizing Flow Models-1

Lecture 21 Normalizing Flow Models-2

Lecture 22 Energy-based Models

Lecture 23 Diffusion Models

Lecture 0 Project-1 MiniGPT

Lecture 0 Project-2 Images Generation

Lecture 0 Project-3 Realistic Images Generation

This course is designed for beginners and intermediate learners who want to master Generative AI with Python,Ideal for: Students, professionals, or hobbyists interested in AI and machine learning.,Developers and data scientists aiming to build real-world AI projects.,Anyone wanting hands-on experience with GANs, VAEs, Transformers, and Diffusion Models.,Learners seeking a career boost or research opportunities in AI, data science, or deep learning.